1. Field of the Invention
The present invention relates generally to power management and more particularly to power usage estimation and monitoring systems.
2. Description of Related Art
Power consumption of servers, memory devices and network equipment is a major issue of concern in the design and operation of data centers. Apart from consuming electrical power, servers and associated equipment must be cooled to ensure that they operate in the envelope of conditions for which they have been designed. Operating outside this envelope reduces the life-span and increases the failure rate of these devices. For every 2 dollars spent powering a server, typically 1 dollar is spent cooling it. Consequently increasing server power efficiency, reduces consumption and the costs of cooling.
Conventional systems employ methods for power estimation that compare real-time data obtained from sensors in a monitored electrical system with data generated by a predictive modeled virtual system. If a predicted value of some electrical or physical entity is outside a set value or an alarm condition value, a re-calibration of the system is generated utilizing mathematical equations with the new real-time data, However, such systems tend to have limited application and are used mainly for monitoring electrical power consumption in large complex networks such as factories, processing plants, ships, etc. However, such conventional systems often cannot cope with electrical systems of a much smaller scale such as electronic systems due to the substantially smaller size. Additionally, the greater variances between different instances of these small systems render it economically and difficult, if not impossible, to install meters into the systems,
Although severs are becoming more power efficient, they are also getting smaller, for example Blade-servers. Consequently, the power density, the amount of electrical power that must be concentrated in a given area of the data center, is increasing. The implications for the data center are increased power demand per unit area of data center space, and a greater cooling capacity requirement.
Another unaddressed problem in data centers arises when servers are being installed in a rack system. Each rack must be adequately provisioned for power, otherwise the power unit supplying a rack may be insufficient. Overestimating the power consumption of servers in a rack, can result in the selection of an inappropriate power unit which is not power efficient, or under-utilization of the rack system capacity. Underestimating the server power demand can result in power failure for the rack. Conventional systems cannot accurately determine the server power demand in a rack without significant increased complexity and cost.
Data centers also operate under a complex range of electricity tariffs, Failure by a center to operate in a predicted, narrow window of power consumption can incur severe financial penalties. Thus, centers have a need to accurately predict future power demands.
Power measurement in data centers is traditionally performed at the rack-level, using a power meter integrated in the PDU (Power Distribution Unit) supplying power to the rack. Alternatively, a meter is externally connected to the supply line of the rack. However, this approach can, at most, achieve only a rack-level power resolution, as it is not possible to determine the power consumption of the individual servers. Another approach uses a radio-transmitter device which measures power through a sensor located around the server power cable. This, and other approaches, incur significantly increased expenses and complexity. Indeed, all of these conventional methods involve physical sensors and have intrinsic limitations when deployed across a population of several thousand servers.
The cost of power meters ranges from $100's to $1000's, Complexity is increased due to the mass of physical wires required to power and connect the meters, and the large number of communication channels or signals. Significantly, it is not possible/practical to determine the power consumption of the individual processes and applications executed on an individual server using external measuring devices. Because dynamic scheduling and execution of multiple processes occurs in micro-second time frames on the server, it is impossible to synchronize the measurement of these activities with an external physical meter.
Conventional power estimation systems are further limited because most of these systems assume a power model that is linear for CPU and memory usage and for network I/O activity. These conventional models are generated using measurements of server power consumption, while executing a series of benchmarks. Linear interpolation is used to obtain a best-fit linear equation which is thought to describe the server's power behavior. However, such equations are naive and produce results which can have an average error of at least 5% and which often deliver results which are 25% or more in error. Because of this level of inaccuracy, these conventional systems cannot be used for billing purposes related to power consumption in most countries, because they fall below the standards required by laws regulating the accuracy of meters used for billing utilities.